Big data from mobile phone use has potential to help international development by providing insights into people's needs, behaviors, and crisis responses. However, privacy concerns, lack of data sharing incentives, and limited human capital for advanced analysis pose challenges. Some organizations are working to address these issues through data sharing partnerships between public, private, and nonprofit sectors and by creating incentives for individuals and companies to contribute anonymized data for social good. If these obstacles can be overcome, mobile data analysis could help governments and aid groups improve services, target resources more efficiently, and respond faster to trends and emergencies.

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Big Data, Big Impact:
New Possibilities for International Development

2. 1
Executive Summary
A flood of data is created every day by the interactions of billions of
people using computers, GPS devices, cell phones, and medical
devices. Many of these interactions occur through the use of mobile
devices being used by people in the developing world, people
whose needs and habits have been poorly understood until now.
Researchers and policymakers are beginning to realise the potential
for channelling these torrents of data into actionable information that
can be used to identify needs, provide services, and predict and
prevent crises for the benefit of low-income populations. Concerted
action is needed by governments, development organisations, and
companies to ensure that this data helps the individuals and
communities who create it.
Special Thanks
The World Economic Forum acknowledges the work of Vital Wave
Consulting in assembling this briefing and the extensive research
conducted by partners such as the Boston Consulting Group, the UN
Global Pulse, the Global Viral Forecasting Initiative, Ushahidi, the
World Bank, Vodafone, and the World Economic Forum‟s ICT Global
Agenda Council on the transformative potential for harnessing big data.

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Financial Services
Data gleaned from mobile money services can provide
deep insight into spending and saving habits across
sectors and regions. Digital payment histories can allow
individuals to build credit histories, making them
candidates for loans and other credit-based financial
services.
Education
Data derived from the use of mobile value-added
services can be used to improve public-sector
understanding of educational needs and knowledge
gaps, allowing more targeted and timely initiatives to
disseminate critical information.
Health
Data collected through mobile devices, whether captured
by health workers, submitted by individuals, or analysed
in the form of data exhaust, can be a crucial tool in
understanding population health trends or stopping
outbreaks (see box on page 5). When collected in the
context of individual electronic health records, this data
not only improves continuity of care for the individual, but
it can be used to create massive datasets with which
treatments and outcomes can be compared in an
efficient and cost effective manner.
Agriculture
Mobile payments for agricultural products, input
purchases and subsidies may help governments better
predict food production trends and incentives. This
knowledge can be used to ensure the availability of
proper crop storage, reduce waste and spoilage, and
provide better information about what types of financial
services are needed by farmers. Mobile use patterns
may also help governments and development
organisations identify regions in distress so that targeted
assistance can be directed to them. Early detection can
help prevent families from leaving their land and further
decreasing agricultural production.
By analysing patterns from mobile phone
usage, a team of researchers in San Francisco
is able to predict the magnitude of a disease
outbreak half way around the world. Similarly,
an aid agency sees early warning signs of a
drought condition in a remote Sub-Saharan
region, allowing the agency to get a head start
on mobilising its resources and save many
more lives.
Much attention is paid to the vital services that
mobile phone technology has brought to billions of
people in the developing world. But now many
policy-makers, corporate leaders and development
experts are realising the potential applications, like
the examples above, for the enormous amounts of
data created by and about the individuals who use
these services.
Sources such as online or mobile financial
transactions, social media traffic, and GPS
coordinates now generate over 2.5 quintillion bytes
of so-called „big data‟ every day
i
. And the growth of
mobile data traffic from subscribers in emerging
markets is expected to exceed 100% annually
through 2015
ii
.
The data emanating from mobile phones holds
particular promise, in part because for many low-
income people it is their only form of interactive
technology, but it is also easier to link mobile-
generated data to individuals. This data can paint a
picture about the needs and behaviour of individual
users rather than simply the population as a whole.
Building user-centric solutions offers compelling
possibilities for providing better access to services in
health, education, financial services, and agriculture
for people living in poverty.
Big Data, Big Impact:
New Possibilities for International Development

4. 3
Likewise, utilising the data created by mobile
phone use can improve our understanding of
vulnerable populations, and can quicken
governments‟ response to the emergence of
new trends. Actors in the public, private, and
development sectors are beginning to recognise
the mutual benefits of creating and maintaining
a „data commons‟ in which this information
benefits society as a whole while protecting
individual security and privacy. But a more
concerted effort is required to make this vision a
reality.
Understanding the Dynamics of the
Data Ecosystem
To turn mobile-generated data into an economic
development tool, a number of ecosystem
elements must be in place. For those
individuals who generate the data, mechanisms
must be developed to ensure adequate user
privacy and security. At the same time,
business models must be created to provide the
appropriate incentives for private-sector actors
to share and use data for the benefit of society.
Such models already exist in the Internet
environment. Companies in search and social
networking profit from products they offer at no
charge to end users because the usage data
these products generate is valuable to other
ecosystem actors. Similar models could be
created in the mobile data sphere, and the data
generated through them could maximise the
impact of scarce public sector resources by
indicating where resources are most needed.
A look at the various types of data and actors in
the data ecosystem illustrates the roles and
incentives at work. The private sector maintains
vast troves of transactional data, much of which
is „data exhaust‟, or data created as a by-
product of other transactions. With the use of
mobile phones, much of this data can be
associated with individuals and their locations.
The public sector in most countries also
maintains enormous datasets in the form of
census data, health indicators, and tax and
expenditure information.
The Internet and mobile revolution have added
yet another source: data contributed by
Data through the Mobile Financial Services
Lens
A look at the „pillars‟ from The World Economic
Forum‟s Mobile Financial Services Development
Report offers insights into how the requirements of
mobile financial services development coincide with
prerequisites for a thriving data commons.
Regulatory Proportionality: Finding appropriate uses
for mobile-generated data will require regulation similar
to that needed for mobile financial services. In both
situations, regulation must keep pace with new
technology and protect consumers without stifling
innovation or deterring uptake. The development of
sensible data standards could increase uptake of both
mobile financial services and individual data security.
Consumer Protection: As with mobile financial
services, proper regulation and data ownership
processes must be put in place to prevent the theft or
misuse of sensitive information.
Market Competitiveness: In the long term, adequate
competition is essential to ensure a wider range of
affordable services and interoperability. However,
private-sector companies should be encouraged to
allow access to non-sensitive data that can benefit
populations and deepen their own understanding of
individual behaviour. Such cooperation may also help
telecom operators realise that creating interoperable
mobile money systems can benefit them over the long
term.
Market Catalysts: For both the data commons and
mobile money, government can serve as a catalyst to
ensure legitimacy. This will require open and
transparent governance, as the idea of government
access to an individual‟s financial information could
discourage uptake of mobile financial services.
End User Empowerment & Access: Individuals must
have a moderate degree of financial literacy, affordable
access to a mobile device, and a mobile network
connection, in addition to control over their own
information.
Distribution and Agent Network: Analysing
transactional data could determine where there is
demand for additional mobile money agents.
Adoption & Availability: Open data can help
determine which finance products are in the highest
demand, matching demand with supply.

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individuals voluntarily or through crowdsourcing.
NGOs like Ushahidi are already using crowdsourcing
to obtain, verify and disseminate real-time
information about natural disasters and election
monitoring, and Ushahidi is developing ways to filter
and use the huge amounts of information being
created with applications such as SwiftRiver
iii
. With
the overwhelming majority of the world now having
access to a mobile phone
iv
, crowdsourcing allows
individuals to contribute to the information gathering
process, making it more democratic and transparent.
The graphic below illustrates the various data types,
incentives, and requirements of actors in this new
data ecosystem.
Closing the Information Gap: Identifying
the Returns from Better Data Use
Already, a number of organisations in the public
and development sectors have embraced the
vision of a data ecosystem in which information
captured from these varied sources is used for
the benefit of global populations. Global Pulse
is a UN initiative aimed at bringing together
expertise from the public, private, development,
and academic sectors to develop approaches
for harnessing data for policy and action. Its
director, Robert Kirkpatrick, says that data
collected through mobile device usage can spur
effective action in two primary ways: by
reducing the time lag between the start of a
trend and when governments and other
authorities are able to respond to them, and by
reducing the knowledge gap about how people
respond to these trends.
Kirkpatrick cites Dr. Nathan Eagle‟s research
v
showing that when mobile operators see airtime
top-off amounts shrinking in a certain region, it
tends to indicate a loss of income in that
population. Such information might indicate
increased economic distress before that data
shows up in official indicators. Meanwhile,
Global Pulse‟s own research into food related
conversations on Twitter has shown very strong
correlations with food price inflation
vi
. “This
information comes from two brand new
sources: what people are doing and what they
are saying,” says Kirkpatrick. “As a government
or aid agency, you might know that food prices
are rising or rains aren't coming, but what if you
could see where and how people are already
• Faster Outbreak
Tracking & Response
• Improved
Understanding of
Crisis Behavior
Change
• Accurate Mapping of
Service Needs
• Ability to Predict
Demand & Supply
Changes
Data
Commons
Individuals
Data Type: „Crowdsourced‟ information, data
exhaust
Sharing Incentives: Pricing/offers, improved
services
Requirements: Privacy standards, „opt out‟
ability
Public/Development Sector
Data Type: Census data, health indicators, tax
and expenditure information, facility data
Sharing Incentives: Improved service
provision, increased efficiency in expenditures
Requirements: Privacy standards, „opt out‟
ability
Private Sector
Data Type: Transaction data, spending & use
information
Sharing Incentives: Improved consumer
knowledge and ability to predict trends
Requirements: Business models, ownership of
sensitive data
Data Mining
& Analysis

6. 5
changing their behaviour and prioritise where
you put resources in response?”
Public health offers one of the most compelling
areas where the analysis of mobile and Internet
data could lead to huge public gains. The San
Francisco-based Global Viral Forecasting
Initiative (GVFI) uses advanced data analysis
on information mined from the Internet to
identify comprehensively the locations, sources
and drivers of local outbreaks before they
become global epidemics. GVFI‟s Chief
Innovation Officer, Lucky Gunasekara, says this
technique can successfully predict outbreaks up
to a week ahead of global bodies such as the
World Health Organisation that rely on
traditional techniques and indicators.
Employing new data collection and analysis
methods could be a less costly, more efficient
method of developing market intelligence for large
organisations like the World Bank. The Bank already
spends millions of dollars each year on statistical
analysis of the needs of the poor
vii
. Smarter data
collection and analysis could free resources for use
in economic development efforts
viii
.
In a time of constrained government resources and
reduced foreign aid, the insight produced by mining
mobile data offers the possibility of preventing crises
and targeting services to the populations that need
them most. Yet there are serious challenges that
need to be addressed before the pieces of the
puzzle fall into place.
Obstacles on the Path to the Data
Commons
Ecosystem actors, like those described above, have
much to gain from the creation of an open data
commons. Yet the sharing of such data especially
that tied to individuals raises legitimate concerns
that must be addressed to achieve this cross-sector
collaboration.
Privacy and security: As ecosystem players
look to use mobile-generated data, they face
concerns about violating user trust, rights of
expression, and confidentiality. Privacy and
security concerns must be addressed before
firms, governments, and individuals can be
convinced to share data more openly.
Data personalisation: When individuals have
multiple SIM cards, it is impossible to aggregate
data from each SIM back to the same individual.
This data is most useful if it can be attached to
demographic indicators, which allow the data to
tell a story about the habits of a segment of the
population. Improved methods of tying
subscriptions to demographic information are
needed to ensure data generated by mobile
devices is as individualised as possible.
Data sharing incentives: Individuals, fearing
security and privacy concerns, often resist
sharing personal data. In addition, many private-
Mobilising Data to Deal with an
Epidemic
In the wake of Haiti‟s devastating 2010
earthquake, researchers at the Karolinska
Institute and Columbia University
demonstrated that mobile data patterns
could be used to understand the movement
of refugees and the consequent health risks
posed by these movements. Researchers
from the two organisations obtained data on
the outflow of people from Port-au-Prince
following the earthquake by tracking the
movement of nearly two million SIM cards in
the country. They were able to accurately
analyse the destination of over 600,000
people displaced from Port-au-Prince, and
they made this information available to
government and humanitarian organisations
dealing with the crisis. Later that year, a
cholera outbreak struck the country and the
same team used mobile data to track the
movement of people from affected zones.
Aid organisations used this data to prepare
for new outbreaks. The example from Haiti
demonstrates how mobile data analysis
could revolutionise disaster and emergency
responses.

7. 6
sector firms do not see an incentive to share data
they regard as proprietary. Governments often
cannot force contractors to share data collected
in the execution of public contracts or make all
government data available for use by academia,
development organisations, and companies. All
players must see material benefits and incentives
in data sharing that outweigh the risks.
Human Capital: Accurate and actionable data
mining and analysis requires considerable
technical skill, and data scientists are both in
short supply and expensive to employ. GVFI‟s
Gunasekara notes that even many large
corporations do not have access to the type of
expertise they need to develop novel data mining
techniques. “Most of these people want to start
their own companies, not work for someone
else,” he says. Maximising the contribution of
human capital requires incentives for these
individuals to use their talents for the public good
along with long-term efforts to grow the talent
pool.
Overcoming the Obstacles:
Novel Approaches
A number of organisations are already working to
overcome the challenges and create the incentive
structures needed for cross-sector cooperation.
Global Pulse is creating a network of Pulse Labs
that bring together experts in government,
academia, the development sector, and private
companies to pioneer new approaches to using data
for development challenges.
The organisation is also now actively engaging with
partners around what Robert Kirkpatrick calls „data
philanthropy‟, where corporations are encouraged to
share anonymised data for use by the public sector
to protect vulnerable populations. These companies
are driven partly by a recognition that more effective
policy action will lead to greater resilience from
economic shocks, and therefore translate into better
business continuity. Athletic apparel company Nike
has demonstrated an approach to corporate data
sharing through its GreenXchange patent-sharing
system. Nike is among the first corporations to
explore opening up data publicly, and plans to share
data on the sustainability of its operations.
In the area of individual incentives, Jana, a Boston-
based start-up, conducts market research for global
organisations in over 50 countries. The company
uses SMS to survey emerging market customers in
exchange for airtime, creating a financial incentive
for consumers to overcome their concerns about
sharing personal data.
Nathan Eagle, Jana‟s founder, notes that the data
created is useful not only to marketing organisations
in private-sector companies, but has extensive
development uses as well. He cites a mobile data
analysis effort in the huge slum of Kibera, outside
Nairobi, that was used to map population change
and direct latrine and water pipe building efforts for
the benefit of the slums residents.
Government as Data Catalyst
Several forward-thinking governments in the
developing world are demonstrating how
government can catalyse the development of this
ecosystem through the opening of its own datasets
and the active management of their dissemination
and use.
In July 2011, Kenya launched its new Open Data
Portal, which includes a full digital edition of the
2009 census, 12 years of detailed government
expenditure data, government household income
surveys, and the location of schools and health
facilities. The portal provides unlimited data access
on the web and through mobile phones to
researchers, web and software developers,
journalists, students, civil society and the general
public. Civic organisations, mobile application
developers, and media groups are already using the
data to improve understanding of population
patterns, increase the transparency of governments,
and map public services.
The World Bank has provided support for the
initiative, but Chris Finch at the World Bank notes
that the Kenyan authorities, with support at the
highest levels of government, drove the initiative
forward. In part, the Kenyan government responded
to the country‟s growing information technology
sector and the new constitution‟s guarantees of
access to information. Finch notes that the
government moved forward with the Open Data
Portal before the legislative, policy and legal

8. 7
framework, including protections for data reuse,
were fully in place, given momentum around
Constitutional guarantees for openness,
transparency, and participation. Policy frameworks
for such protections are now being backfilled.
Finch sees the role of government as setting the
legal frameworks governing data privacy and
security, and also in developing systems that allow
various agencies and ministries to continually
update the data they make available. The
development community can encourage this
behaviour by supporting progressive governments
such as Kenya‟s and linking them to the technical
and financial resources they need. Unfortunately, in
many countries, governments are frequently seen as
part of the challenge to establish a productive data
commons. Kenya‟s example demonstrates that
government can take the lead.
Juliana Rotich of Ushahidi also notes that
governments must invest in applications that make
the data they are releasing useful. “Beyond
providing the data online, governments need to
invest in applications that make the data accessible
and useful to citizens,” she says. Rotich cites the city
of Chicago‟s Open Data portal as a notable example
of a government opening data in the context of a
robust multi-sector effort.
Call to Action
To realise the mutual benefits of creating an
environment for sharing mobile-generated data, all
ecosystem actors must commit to active and open
participation. Governments can take the lead in
setting policy and legal frameworks that protect
individuals and require contractors to make their
data public. Development organisations can
continue supporting governments and demonstrating
both the public good and the business value that
data philanthropy can deliver. And the private sector
can move faster to create mechanisms for the
sharing of data that can benefit the public.
Despite the challenges and risks, the opportunities
Government Catalyst
• Enact appropriate legislation protecting end users
without stifling innovation
• Open data to the public (free or for purchase) in a
way that allows for innovation without infringing on
citizen's privacy
• Encourage the development of appropriate
technological infrastructure and training of
individuals capable of analyzing big data
Private Sector Development
• Once proper regulations are in place and public
trust about the use of data has been gained,
telecoms can compile or 'curate' mobile-generated
data for use by both profit-seeking enterprises and
development organisations
Public-Private Collaboration
• Telecoms and governments must work together
to find a way to track mobile information back to
an individual, rather than a SIM
• Government or Multi-lateral funded initiatives
using data generated from mobile for
development or government planning purposes
(e.g., health, agriculture, education)

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available to better serve individuals in emerging
markets should outweigh these risks. 

10. 9
For more information please visit:
World Economic Forum Mobile Financial
Services Development Report
www.weforum.orgmfs
Global Viral Forecasting
www.gvfi.org
UN Global Pulse
www.unglobalpulse.org
Ushahidi SwiftRiver Platform
ushahidi.com/products/swiftriver-platform
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i
www-01.ibm.com/software/data/bigdata/
ii
www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns70
5/ns827/white_paper_c11-520862.html
iii
/ushahidi.com
iv
www.itu.int/ITU-D/ict/facts/2011/material/ICTFactsFigures2011.pdf
v
ess.santafe.edu/publications.html
vi
www.unglobalpulse.org/projects/twitter-and-perceptions-crisis-
related-stress
vii
/www.nextbillion.net/blog/the-age-of-big-data
viii
jana.com/research/sample/,
www.nextbillion.net/blog/2011/10/05/reaching-the-next-billion-
through-mobile
Haiti disaster example reference is at Karolinska Institutet
(www.ki.se)